Death of a Salesman: Account Acquisition in a New Environment
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Death of a Salesman: Account Acquisition in a New Environment

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The nature of sales in retail banking has changed dramatically. While there is a renewed pressure to grow accounts, the techniques banks have traditionally used to acquire new accounts have become ...

The nature of sales in retail banking has changed dramatically. While there is a renewed pressure to grow accounts, the techniques banks have traditionally used to acquire new accounts have become less effective.

As consumer preferences continue to shift and non-traditional competitors continue to disrupt the market, the ROI of acquisition techniques like batch mail and branch cross-sell will continue to decline. In order to thrive, banks need to leverage the tremendous amount of data they have on each of their customers to drive more profitable and satisfying customer interactions across all of their channels.

This presentation will:
• Identify the market trends impacting banks’ growth strategies.
• Explore the role of marketing and risk analytics in making better acquisition decisions.
• Introduce best practices for implementing a more holistic approach to account acquisition.

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  • Financial institutions have found that the path to sustainable profitability is a long and winding road fraught withunpredictable obstaclesThe recent macroeconomic climate has far‐reaching implications for FIs. Prior to the economic crisis, larger FIs inthe U.S. sought return on equity (ROE) approaching 20%. Today, most are struggling to raise ROE to 12% fromlevels that plunged as low as 4% during the depths of the financial crisis in 2008.Meanwhile, because customers are becoming more savvy and demanding in their relationships with their FIs, retail customer profitability has declined by between 5% and 15% at some firms.Low interest rates have squeezed net interest margins and contributed to a drop in net interest income, a widelywatched barometer of the overall financial condition of FIs. The increased costs of meeting more stringentregulatory requirements, including greater liquidity and capital requirements, along with heightened customerservice needs, serve to squeeze profit levels.
  • The ultimate goal for FIs is to differentiate themselves and strive to be a primary financial institution.Studies has shown that once an FI earns the coveted primary FI status, those accounts can be 2-6 times more profitable (and in some cases higher) than the average account.
  • As we can see in this slide, about 90% of respondents are reasonably content, or not inclined to change because of “sticky” services, such as online and mobile banking and billpay.Some of these customers and members will be yours, so offering an improved experience increase both retention and acquisition efforts. It will take FIs with superior products and services and an outstanding overall customer experience to unseat incumbents, but the effort will be worth it.
  • Alex

Death of a Salesman: Account Acquisition in a New Environment Death of a Salesman: Account Acquisition in a New Environment Presentation Transcript

  • Death of a Salesman:Account Acquisition in a New Environment April 2, 2013 Zoot Enterprises, Inc. Proprietary & Confidential Information.
  • Ed O’Brien Director Banking Channels Mercator Advisory Group Keith Shields Chief Analytics Officer, MagnifyChief Credit Officer, Loan Science Tom JohnsonVice President, Strategic Alliances Zoot Enterprises 2
  • AGENDA• Industry Overview• More Intelligent Decisions through Analytics• Next Generation Account Acquisition• Q&A 3
  • CHANGING MARKET CONDITIONS SIGNIFICANTLY IMPACT FI GROWTH STRATEGIES• Financial Institutions are under intense pressure to perform, even though the business fundamentals are challenging • Reduced fee income • Increased costs • Reduced revenues, net interest income, and profitability• FIs are facing intense pressure to increase their financial performance throughout their LOBs and throughout their portfolios• They need to find ways to profitably grow their portfolios in new and creative ways 4
  • CONSUMERS ARE CONSOLIDATING THE NUMBER OF INSTITUTIONS THEY USE Mean Number of Financial Institutions Used by Households by Type (Base = Those with FI relationships by type of FI) 2012 (4.9 mean of all financial institutions) 4.8 2011 (4.9 mean of all financial institutions) 2010 (6.3 mean of all financial institutions) 2.6 2.7 2.7 2.5 2.3 2.1 2 2.11.6 1.6 1.6 1.6 1.4 1.3 1.4 1.4 1.4 1.4 1.4 1.2 1.2 1.2 1.2 1.2 1.3 1.2Full service banks Credit card banks Mortage lenders Credit unions Auto lenders Brokerage firms Online only bank Online Other brokerage/investment 5
  • CONSUMERS ARE MORE LOYAL TO THEIR PRIMARY FIHave You Changed Your Primary Financial Institution in the Past Two Years? (Base = All) 88% 90% 86%201020112012 14% 12% 10% Yes No 6
  • MOST PREFER IN-PERSON COMMUNICATION WHEN LEARNING ABOUT NEW FINANCIAL PRODUCTSPreferred Method for Becoming Aware of New Financial Products and Services (Base = All) In person with an account specialist 28% In person with teller or greeter 23% Electronically at ATM or kiosk 7% Telephone call with account specialist 6% Chat online at FI website 6% Teller-assisted videoconference 2% Other 19% None of the above 8% 7
  • INDUSTRY OVERVIEW: FI PROFITABILITY SOLUTION POSITIONING Data Cleansing Customer Business and Quality CRM Analytics Predictive Intelligence Analytics Integration Layer Channel Systems Core Banking System LayerUnderlying FIInfrastructure Application Server Layer Database Layer 8
  • INDUSTRY OVERVIEW: VARYING PROFITABILITY Database/ PERSPECTIVES Data LOB and Consulting Strategy Warehouses FI-Centric Legacy Approaches Partners Consulting Systems Best Financials Practices Systems Reviews Profitability Operations FI Profitability Analytics Systems Customer Channels Analytics SystemsISV Products FI/ISV and BI, Reports, Data Partnerships Consulting KPIs, and Cleansing Marketing Services Dashboards and Quality and CRM Systems 9
  • INDUSTRY OVERVIEW: COMMON CATEGORIES OF ANALYTICS SYSTEMS Business Systems Channel Data Customer Decisioning and Mgmt Mgmt Insight Models Data Systems Sources• Databases • Metadata • Predictive analytics • Real-time • Branch • Customer decisioning • ATM• Data warehouses • Master data management experience • Content • Online• Data marts • Profitability models management• Core systems • Data modeling • Mobile • Risk and compliance • Campaign• CRM • Business • Call centers models management intelligence• Web • Network analytics • Event • Multichannel • Dashboards• Social media management • Visualization • Reporting tools • Querying capabilities 10
  • ANALYTICS-DRIVEN DECISIONS• Why do banks (or any lender) invest in analytics? • Applying analytical techniques, particularly predictive modeling, to customer data gives forward-looking insight into customer behavior.• Understanding future customer behavior is integral to making better decisions and driving lender profitability from two primary perspectives: 1. Marketing / Pricing – What loan parameters (APR in particular) acquire the customer’s business? 2. Credit Risk Management – Will the customer default on the loan? Is his business worth having?• Death of a Salesman? Possibly. • The renewed appetite for profitable growth (note Ed’s presentation), combined with the explosion of available customer data, make the time right for automatic, realtime, analytically- informed lending to customers. 11
  • MARKETING AND CREDIT RISK APPLICATIONS • The need for analytics within the Marketing and Credit Risk Management disciplines is pervasive. • A recent survey of business technology professionals (see below) indicates that much of the interest in Big Data and Analytics is driven by (or at least correlated with) Marketing or Risk Management needs.MARKETING CREDIT RISK NEEDS NEEDS Data: Information Week Analytics, Business Intelligence and Information Management Survey of 417 business technology professionals at companies using or planning to deploy data analytics, BI or statistical analysis software, October 2012 12
  • MARKETING ANALYTICS & CREDIT RISK ANALYTICS• So lenders can make better decisions and drive profitability through “Credit Risk Analytics” and “Marketing Analytics” (not exclusively of course).• Let’s define these terms that we’ll use colloquially throughout the presentation: • Credit Risk Analytics: empirically-based quantitative techniques (e.g. statistical models) aimed at understanding, predicting, and controlling the level of credit risk associated with a consumer loan applicant and/or portfolio • Marketing Analytics: empirically-based quantitative and qualitative techniques (e.g. statistical models, segmentation) aimed at understanding, predicting, and classifying the likely purchase behavior of a consumer or group of consumers 13
  • THE IMPORTANCE OF CREDIT RISK ANALYTICS• Let’s show the importance Credit Risk Analytics with an example: • If a lender makes a $100 profit on a paying loan and loses $400 on a defaulting loan, then it has to book 4 paying loans for every defaulting loan just to break even. • Another way to state the above bullet is this: a loan applicant should have at least an 80% chance (4:1) of paying as agreed to be considered for approval. • How do we determine if an applicant has at least an 80% chance of paying as agreed? Empirically-derived, demonstrably and statistically sound models of course. Almost all lenders use these in some form… • Generic credit bureau scores -and/or- • Custom scores derived from contract attributes (LTV, PTI) and credit bureau attribute libraries (from Zoot of course) 14
  • RISK MODELS• Continuing with the example…the importance of robust, predictive “risk models”: • So what if a lender is drawing from a population that is inherently 85% good (85% will pay off a standard loan) and 15% bad (15% will default on a standard loan)? Shouldn’t that lender always be profitable? • It is crucial that the statistical model (or something equivalent) used by the lender to predict the likelihood of default be able to SEPARATE the good from the bad. Example below: • If the model is incapable of any separation whatsoever, it will issue a 15% probability of default (PD…in bank terminology) for every proposed contract. This is less than a 20% cutoff, so we approve everything. • Thus the lender’s profitability is easy to calculate…suppose 1,000 contracts will be booked: P = 850*$100 – 150*$400 = $25,000 … is it that easy to make money? By always saying yes to loan applications? That would be Death of a Credit Analyst. But it’s not that easy… 15
  • “GOOD AND BAD I DEFINE THESE TERMS, QUITE CLEAR, NO DOUBT, SOMEHOW” -- BOB DYLAN• Risk models must, first and foremost, distinguish between good and bad (i.e. rank order the risk): • “All models are wrong, but some are useful”. -- George E.P. Box • No individual customer has a 15% chance of default. All individual customers effectively have a 0% chance or 100% chance of default (they either do or they don’t). • Profitability is far greater if the model is able to issue higher PD predictions for defaults than for payers. This is what happens when a model is able to RANK ORDER the risk. See the next bullet. • Back to our example: Suppose the model predicts a PD of 25% for half the defaults, and 10% for the other half. In turn it issues a PD prediction of 25% for ¼ of the payers, and 10% for ¾ of the payers. • Before we calculate profitability we’ll note that the profitability on all applicants with a predicted PD of greater than 20% is $0. They are declined based on the breakeven calculation on the previous slide. So… • P = [850*.25*$0 + 850*.75*$100] – [150*.5*$0 + 150*.5*$400] = $33,750 • This is a 35% increase in profitability due to having a better predictive model. 16
  • MARKETING ANALYTICS• We’ll continue the discussion with Marketing Analytics, which has become a staple in the retail industry (Target for example)… • The analytical techniques used to predict how “in-market” a customer is for clothes, diapers, etc… can be the same ones used to predict how “in-market” a customer is for a loan. • The advantage of using predictive analytics to identify the customers most likely to take up a loan is that it “expands the base of incrementality” associated with a loan offer. • In other words, identifying the groups of customers that are most likely to buy (take up a loan) is tantamount to identifying the groups that contain the majority of the “incremental” sales (contracts). • If I can get the most of the incremental sales by making an offer to only a small fraction of the population, then I can squeeze most of the benefit from the offer at a fraction of the cost. Example next slide. 17
  • ADDING BUSINESS VALUE THROUGH MARKETING ANALYTICS: AN EXAMPLE • Intelligent use of Marketing Analytics enables lenders to generate incremental loans cost-effectively and efficiently. An example with assumptions: • An untargeted (no model), incentivized loan offer to 100,000 customers increases the “take rate” by 10%. • The revenue per incremental loan is $250. • The cost of communicating the offer to 100,000 consumers is $20,000. • The cost of the incentive is $20 per loan. • When we apply a predictive model, we split the population into 4 groups (1=most likely to take…4=least likely to take) Organic Offer Incremental Incremental Incremental Incremental No Model Population Take Rate Take Rate “Takers” Revenue Cost Profit Profit = $10,000 100,000 10% 11% 1,000 $250,000 $240,000 $10,000 Model Organic Take Offer Incremental Incremental Incremental Incremental Population Rank Rate Take Rate “Takers” Revenue Cost Profit Use a Model 1 25,000 18% 19.80% 450 $112,500 $104,000 $8,500 2 25,000 14% 15.40% 350 $87,500 $82,000 $5,500 Profit = $14,000 3 25,000 6% 6.60% 150 $37,500 $38,000 ($500)40% improvement 4 25,000 2% 2.20% 50 $12,500 $16,000 ($3,500) 18
  • THE INTERSECTION OF MARKETING AND CREDIT RISK• Practically we should not deploy Marketing Analytics in a lending environment without doing sound Credit Risk Analytics at the same time. • Marketing analytic efforts are typically aimed at increasing response (and thus sales). Doing so can also increase credit risk, which means credit losses can easily wipe out the gains had by improvements in targeted marketing efforts.• The right loan offer needs to be defined as the one that maximizes incremental profit…after incremental credit losses are factored in. • Making the right loan offer is an analytical exercise that requires the intersection of Marketing and Credit Risk Analytics. Through the Magnify-Loan Science partnership, we specialize in this type of exercise…and we deploy through Zoot.• We see pre-approval models as being the perfect example of this intersection. And we will show the work we’ve done in auto… • Common with credit cards…interest rates and credit limits are tested to determine the impact on response and yearly interest revenue. • Pre-approval models for auto are more complex, because the presence of collateral means we have to solve for very important variables like loan-to-value and term. 19
  • DEATH OF REDEFINING A SALESMAN: USE ANALYTICS TO TARGET OFFERS• The tyranny with almost all pre-approval programs is that the customers who respond to them are the ones you least want to give credit to.• Customers with low credit bureau scores are generally the ones that respond to pre-approval offers, and the more exposure the lender is willing to take, the better they respond. • See the example below from an auto captive…the data are doctored somewhat but not to the point where the message is changed: Population: Existing Customers and Prospects FICO Score and Control Target Buy Pre-Approval Amount Buy Rate Rate “Incrementality” Lift FICO <= 680 and Pre-Approval >= $30,000 1.93% 2.17% 0.23% 12.14% FICO <= 680 and Pre-Approval < $30,000 0.93% 1.52% 0.60% 64.27% FICO > 680 and Pre-Approval < $30,000 2.34% 2.32% -0.01% -0.46% FICO > 680 and Pre-Approval >= $30,000 2.64% 2.56% -0.08% -2.85% 20
  • MAKE THE RIGHT OFFER RESPONSIBLY… • Can we have the high lift associated with high-risk customers AND control the risk of the pre-approved portfolio? Yes, probably so. Consider turning the traditional PD (probability of default) model on its head: • Traditional: PD = f(credit score, LTV, PTI, term,…) • Pre-approval: LTV = f(PD, credit score, PTI, term,…) • See the auto captive example below, where we control for PD and solve for LTV Will yield Tier B performance. Will yield Tier C performance. Will yield Tier D performance. Tier B rate can be guaranteed in Tier C rate can be guaranteed in Tier D rate can be guaranteed in the pre-approval offer. the pre-approval offer. the pre-approval offer.Credit Score PD Term LTV Limit Credit Score PD Term LTV Limit Credit Score PD Term LTV Limit 581-600 4.0% 60 60% 581-600 8.0% 60 75% 581-600 15.0% 60 90% 601-620 4.0% 60 75% 601-620 8.0% 60 85% 601-620 15.0% 60 98% 621-640 4.0% 60 85% 621-640 8.0% 60 94% 621-640 15.0% 60 105% 641-660 4.0% 60 95% 641-660 8.0% 60 100% 641-660 15.0% 60 112% 661-680 4.0% 60 100% 661-680 8.0% 60 105% 661-680 15.0% 60 120% 21
  • REDEFINING SALES OCCURS WHEN THE BENEFITS OF ANALYTICS AND TECHNOLOGY ARE TANGIBLE…• In the example on the previous slide we achieved two important outcomes: 1. We confined our targeting to the customers who, according to our best Marketing Analytics, would respond to our offer. 2. We confined our offers to those that, according to our best Credit Risk Analytics, would be profitable at a controlled level of risk and price.• We also achieved a third very important outcome, which I’ll offer as a conclusion: we generated incremental loans that subsequently contributed an additional $8 mils profit per year.• But what is missing from this good story? DEPLOYMENT. Analytic tools and technologies must be made available to operational systems when: • Credit decisions are made, or when • A list of targeted customers is generated, or when • The parameters of pre-approval offers are specified• ... and this is where Zoot fits in: realtime deployment of analytic tools so that interactions with the customer are informed, targeted, and profitable… 22
  • NEXT GENERATIONACCOUNT ACQUISITION 23
  • CONSISTENT EXPERIENCE ACROSS CHANNELS 24
  • TYING IT ALL TOGETHER: OFFERS REPOSITORY 25
  • OPTIMIZED OFFERS 26
  • BETTER DATA, BETTER DECISIONS 27
  • QUICKER TIME TO MARKET 28
  • SUMMARY• Sales in retail banking isn’t dead, but it has changed.• New channels and more “The only thing you got in this interactions across all channels. world is what you can sell. And the funny thing is that youre a salesman, and• Analytics available to make you dont know that.” more intelligent decisions. ~Arthur Miller Death of a Salesman• Underlying technology must support next generation account acquisition techniques. 29
  • QUESTIONS ?Ed O’Brien, Director Banking Channels, Mercator Advisory Groupeobrien@mercatoradvisorygroup.comKeith Shields, Chief Analytics Officer at Magnify Analytic Solutions andChief Credit Officer at Loan Sciencekshields@MarketingAssociates.comTom Johnson, Vice President, Strategic Alliances, Zoot Enterprisestom@zootweb.com 30